The present invention relates to the field of medical treatments and, more specifically, to predicting treatment efficacy and determining optimal treatment for any illness, disease or abnormality including psychiatric and neurological disorders. It also relates to the field of medical and clinical cognitive systems and methods of performing medical diagnosis and estimating and assessing the type, severity, level or critical medical/clinical parameters of any illness, disease, disorder or condition.
Major depressive disorder (MDD) is a serious mental disorder and is now the third largest cause of workplace disability. It has been estimated that by the year 2020, depression may account for 15% of total global disease burden, second only to ischemic heart disease.
Despite the severity of MDD, the procedure for selecting optimal treatment is not well developed. The choice of antidepressant therapy is currently based on personal preference, weighted by clinical factors such as family history, symptom clustering and previous medication history. However, it is not uncommon for the first medication used to prove ineffective and the increasingly despondent patient is subjected to a series of medication trials before one that works is found. Since an adequate antidepressant trial should be of at least 4 to 6 weeks duration, the personal and economic cost of delayed or ineffective therapy is substantial. Clearly, a method of reliably choosing an effective treatment before the initial trial would be of immense clinical and economic value.
Moreover, although patients may appear to have similar clinical characteristics, a treatment that works for one patient, may not work well for others. This suggests that current diagnostic systems and testing procedures may not be sufficiently sensitive to detect subtle but highly relevant differences between patients presenting with similar complaints.
It is also difficult to make full use of the test data provided using current technologies. The wide array of psychological, physical, hematological, radiological and other laboratory tests generate a very large information set that the busy clinician may find challenging to compile and process. The vast amounts of data that may be generated are typically viewed in isolation or as part of simple syndromatic clusterings. It is possible that a great deal of salience with respect to diagnosis and treatment that is embedded in these data may not be extracted using these basic analytic methods. The abundance and complexity of this information requires a new approach to data management and analysis methods to assist the physician/clinician to make diagnosis and treatment decisions with greater accuracy and efficiency.
For example, mental and neurological illnesses such as mood disorders, schizophrenia, anxiety disorders, epilepsy, Parkinson's disease and dementias of Alzheimer's and other types are common and debilitating conditions for which current treatment algorithms lack precision. The process for assessing an effective therapy (specifically, medication therapy) is poorly defined at best. In short, the basic procedure is to prescribe a therapy on a trial and error basis until one is found that is effective.
Furthermore patients must often wait lengthy periods of time before seeing the clinical experts who possess the skill to effectively treat these conditions, particularly in rural areas where such specialists are few in number. As a result family physicians are often obliged to initiate treatment themselves without the benefit of the extensive experience and knowledge possessed by psychiatrists and neurologists. Even among the clinical experts it is acknowledged that patients meeting the diagnostic criteria for most psychiatric and neurological conditions are not uniformly responsive to the same treatment. Some patients respond well to a given treatment while others, with very similar clinical features, do not.
Treatment failure may be a function of extraneous factors such as treatment adequacy (in terms of medication dose and duration of treatment), poor absorption of oral medication, unusual medication metabolism or inadequate patient adherence to prescribed treatment. However, individual patients often fail to respond to a particular treatment whose efficacy has been demonstrated in large clinical trials. This suggests that biological subtypes may exist within a given syndrome or diagnostic category. Patients afflicted with a particular biological subtype of a condition or diagnosis may respond preferentially to only some of the many medication treatments available to treat that condition or diagnosis. Often, even expert clinicians cannot readily distinguish these illness subtypes using current methodology. This suggests that current diagnostic systems and testing procedures may not be efficiently exploring the information to detect subtle but highly relevant differences between patients presenting with similar complaints.
This phenomenon is readily apparent for patients with major depressive disorder (MDD) treated with antidepressant medications. For the first trial of antidepressant medications the remission rate may be as low as 28%. While up to 67% of patients with MDD will eventually respond, this may require several different antidepressant drug trials, each of several weeks duration. Similar problems determining optimal treatment apply to other psychiatric illnesses such as bipolar disorder, postnatal depression and schizophrenia or neurological conditions such as Parkinson's disease, epilepsy, stroke, brain tumor, Alzheimer's disease and other forms of dementia.
The first step in determining efficient treatment is establishing a correct diagnosis. This can be a more difficult task than it might seem. Specific symptoms can appear in more than one diagnostic category and diagnostic criteria can overlap to the point where confident differentiation of one condition from another is impossible. For example, though clinical acumen has been evolving over generations, selection of optimal antidepressant treatment on the basis of a standard psychiatric assessment remains an elusive objective.
Several attempts have been made to develop improved methods of determining effective treatment. For example, U.S. Pat. No. 7,177,675 and US Patent Application 2008/0125669 describe a method and system for utilizing neurophysiologic information obtained by techniques such as quantitative electroencephalography (QEEG, or EEG) and magnetoencephalography (MEG) in appropriately matching patients with therapeutic entities. In particular, methods for comparing neurophysiologic information relative to a reference set are disclosed along with database-based tools for deducing therapeutic entity actions on particular patients such that these tools are accessible to remote users. However, U.S. Pat. No. 7,177,675 merely discloses simple mathematical techniques that do not efficiently capture the complexity inherent in the data and in the problem and do not have flexibility in computational and modeling structure. Furthermore, this patent emphasizes that prediction of treatment response is best done using EEG data alone without any reference to diagnosis or symptomatic presentation. Although it is acknowledged that current diagnostic systems do not explain all of the variance in treatment response seen in clinical practice, removing diagnosis as the fundamental staring point of treatment planning disregards extensive research evidence and clinical experience indicating that symptomatic features, family history, personality, psychological attributes, social context and other clinical features can be useful in predicting treatment response. Although an extensive list of medication therapies are specifically mentioned in the patent, this list does not include prediction of response to repetitive transcranial magnetic stimulation (rTMS or TMS) therapy.
The method claimed in U.S. Pat. No. 7,177,675 is not inherently adaptive and self-improving.
U.S. Pat. No. 7,231,245 describes a system and method that assesses neurological conditions and predicts responsiveness to medication using some linearly defined features and indices. This patent also describes a system and method that produces features and indices that indicate the presence or absence of a disease or condition, or of the progression of a disease or condition. Such features and indices are derived from electroencephalography (EEG) variables obtained from time domain, power spectrum, bispectrum and higher order spectrum values that are derived from biopotential signals taken from the subject being tested. This patent describes a “differential testing methodology” comprising an infusion device capable of administering a controlled dose of a pharmacological agent. The EEG signal changes induced by the drug infused are then examined.
There remains however, an unmet need for a method with improved accuracy and efficiency of the prediction and estimation models. Thus, it is an objective of the present invention to propose a solution which resolves or at least alleviates one or more of the problems identified above.